Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

Climate change resulting from CO_2 emissions has become an important global environmental issue in recent years.Improving carbon emission performance is one way to reduce carbon emissions.Although carbon emission performance has been discussed at the national and industrial levels,city-level studies are lacking due to the limited availability of statistics on energy consumption.In this study,based on city-level remote sensing data on carbon emissions in China from 1992–2013,we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance.The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance.The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations.However,the overall level of carbon emission performance remains low,indicating great potential for improvements in energy conservation and emission reduction.The spatial pattern of urban carbon emission performance in China can be described as"high in the south and low in the north,"and significant differences in carbon emission performance were found between cities.The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable,resulting in a"club convergence"phenomenon.Furthermore,neighborhood backgrounds play an important role in the transfer between carbon emission performance types.Based on the prediction of long-term trends in carbon emission performance,carbon emission performance is expected to improve gradually over time.Therefore,China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals.Meanwhile,neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth,energy conservation,and emission reductions to realize low-carbon construction and sustainable development.     

中国能源消费碳排放强度及其影响因素的空间计量(英文)

The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%–45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spatiotemporal dynamics and dominating factors of China's carbon intensity from energy consumption in 1997–2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China's carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP(11.72%). Secondly, the trend of Moran's I indicated that China's carbon intensity has a growing spatial agglomeration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel econometric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China's carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.     

Measurement for coordinated development of "four modernizations" and its efficiency of prefecture level cities or above in China

The efficient and coordinated development of industrialization, urbanization, informatization and agricultural modernization(so called "Sihua Tongbu" in China, and hereinafter referred to as "four modernizations") is not only a practical need but also an important strategic direction of integrating urban-rural development and regional development in recent China. This paper evaluated the comprehensive, coupling and coordinated developmental indices of "four modernizations" of China's 343 prefecture-level administrative units, and calculated their efficiency of "four modernizations" in 2001 and 2011. The efficiency evaluation index system was established. The efficiencies and their changing trend during the period 2001–2011 were investigated using the data envelopment analysis(DEA) model. Spatial-temporal pattern of the efficiency of China's prefecture-level units was explored by using exploratory spatial data analysis(ESDA). Finally, the main influencing factors were revealed with the aid of geographically weighted regression(GWR) model. Results indicate that the comprehensive, coupling and coordinated developmental indices and efficiency of "four modernizations" of China's prefecture-level administrative units have obvious spatial differences and show diverse regional patterns. Overall, the efficiency is relatively low, and only few units with small urban populations and economic scale are in DEA efficiencies. The efficiency changing trends were decreasing during 2001–2011, with a transfer of high efficiency areas from inland to eastern coastal areas. The difference between urban and rural per capita investment in fixed assets boasts the greatest influence on the efficiency.     

中国能源消费碳排放的空间计量分析(英文)

Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support,this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy con-sumption,spatial autocorrelation analysis of carbon emissions,spatial regression analysis between carbon emissions and their influencing factors.The analyzed results are shown as follows.(1) Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly.(2) The global spatial autocorrelation of carbon emissions from energy consumption in-creased from 1997 to 2009,the spatial autocorrelation analysis showed that there exists a "polarization" phenomenon,the centre of "High-High" agglomeration did not change greatly but expanded currently,the centre of "Low-Low" agglomeration also did not change greatly but narrowed currently.(3) The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population,R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population.The contribution of population to carbon emissions in-creased but the contribution of GDP decreased from 1997 to 2009.The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population,so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.     

Analysis and prediction of household carbon emission in northwest China北大核心CSCD

The economic development, living standard of residents and carbon emissions in Northwest China are lower than the national average. However,with the favorable policies the economic development is being improved and the household living standard is gradually raised up which will lead to an increase of the residents living carbon emissions, and the emission pattern will also be affected. This is detrimental to the fragile ecological environment of the Northwest China. At present, most of the researches on residents' carbon emissions are focused on the eastern and southern regions of China where there are frequent and significant human activities and high carbon emissions, and less attention has been paid to the northwest region, but the increase of carbon emissions and the increase of environmental costs have a more far-reaching impact on the less developed areas. In addition, when researchers pay attention to the prediction of residents' carbon emissions, they usually focus on the quantitative prediction and ignore the spatial pattern prediction, which is not conducive to the coordinated development between regions. Based on the data of energy consumption and consumption expenditure in the five provinces of Northwest China, including Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang from 1997 to 2016, this paper firstly used the direct coefficient method to measure the residents' direct carbon emissions, and the input-output method to calculate the indirect carbon emissions of the residents and analyzes the present situation of residents' carbon emissions in the northwest region. Secondly, based on standard deviation ellipse and Autoregressive Integrated Moving Average Model, the carbon emissions of residents in Northwest China were predicted in terms of quantity and spatial pattern from 2017 to 2021. Major results are listed as follows: From 1997 to 2016, household carbon emissions in Northwest China showed a rising trend with an initial slow pace followed by a quick pace. The direct carbon emissions were stabilized in the range from 0. 3 × 108 t to 0. 4 × 108 t,and the indirect carbon emissions reached 2. 38 × 108 t. The spatial distribution of household carbon emissions in Northwest China was generally steady with a direction pattern from northwest to southeast. And the moving trend of standard deviation ellipse was from northwest to southeast to northwest, and the center of standard deviation ellipse moved around the point of (99. 07 °E,38. 19°N). From 2017 to 2021, the direct household carbon emissions in Northwest China reach to 0.543 × 108 t and the indirect carbon emissions are 3. 631 × 108 t by 2021. With the development of the western region in China and the promotion of poverty alleviation,Xinjiang Province had a lower emission than Shaanxi,but it had the higher growth rate than Shaanxi. These factors are all driving the main areas of carbon emission northwestward. The purpose of this paper is to recommend how to coordinate between the population and consumption and the environment, leading citizens to establish the value of low-carbon consumption. © 2019 Science Press (China). All rights reserved.     

中国城镇化水平、经济增长和二氧化碳排放的面板协整和因果分析（英文）

Elucidating the complex mechanism between urbanization,economic growth,carbon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a balanced panel data of 31 provinces in China over the period 1997–2010,this study empirically examines the relationships among urbanization,economic growth and carbon dioxide(CO2) emissions at the national and regional levels using panel cointegration and vector error correction model and Granger causality tests. Results showed that urbanization,economic growth and CO2 emissions are integrated of order one. Urbanization contributes to economic growth,both of which increase CO2 emissions in China and its eastern,central and western regions. The impact of urbanization on CO2 emissions in the western region was larger than that in the eastern and central regions. But economic growth had a larger impact on CO2 emissions in the eastern region than that in the central and western regions. Panel causality analysis revealed a bidirectional long-run causal relationship among urbanization,economic growth and CO2 emissions,indicating that in the long run,urbanization does have a causal effect on economic growth in China,both of which have causal effect on CO2 emissions. At the regional level,we also found a bidirectional long-run causality between land urbanization and economic growth in eastern and central China. These results demonstrated that it might be difficult for China to pursue carbon emissions reduction policy and to control urban expansion without impeding economic growth in the long run. In the short-run,we observed a unidirectional causation running from land urbanization to CO2 emissions and from economic growth to CO2 emissions in the eastern and central regions. Further investigations revealed an inverted N-shaped relationship between CO2 emissions and economic growth in China,not supporting the environmental Kuznets curve(EKC) hypothesis. Our empirical findings have an important reference value for policy-makers in formulating effective energy saving and emission reduction strategies for China.     

中国不同区域能源消费碳足迹的时空变化(英文)

Study on regional carbon emission is one of the hot topics under the background of global climate change and low-carbon economic development, and also help to establish different low-carbon strategies for different regions. On the basis of energy consumption and land use data of different regions in China from 1999 to 2008, this paper established carbon emission and carbon footprint models based on total energy consumption, and calculated the amount of carbon emissions and carbon footprint in different regions of China from 1999 to 2008. The author also analyzed carbon emission density and per unit area carbon footprint for each region. Finally, advices for decreasing carbon footprint were put forward. The main conclusions are as follows: (1) Carbon emissions from total energy consumption increased 129% from 1999 to 2008 in China, but its spatial distribution pattern among different regions just slightly changed, the sorting of carbon emission amount was: Eastern China > Northern China > Central and Southern China > Southwest China > Northwest China. (2) The sorting of carbon emission density was: Eastern China > Northeast China > Central and Southern China > Northern China > Southwest China > Northwest China from 1999 to 2003, but from 2004 Central and Southern China began to have higher carbon emission density than Northeast China, the order of other regions did not change. (3) Carbon footprint increased significantly since the rapid increasing of carbon emissions and less increasing area of pro-ductive land in different regions of China from 1999 to 2008. Northern China had the largest carbon footprint, and Northwest China, Eastern China, Northern China, Central and Southern China followed in turn, while Southwest China presented the lowest area of carbon footprint and the highest percentage of carbon absorption. (4) Mainly influenced by regional land area, Northern China presented the highest per unit area carbon footprint and followed by Eastern China, and Northeast China; Central and Southern China, and Northwest China had a similar medium per unit area carbon footprint; Southwest China always had the lowest per unit area carbon footprint. (5) China faced great ecological pressure brought by carbon emission. Some measures should be taken both from reducing carbon emission and increasing carbon absorption.     

中国碳排放对国际贸易影响的经验分析(英文)

International trade is an important impact factor to the carbon emissions of a country.As the rapid development of Chinese foreign trade since its entry into the WTO in 2002,the effects of international trade on carbon emissions of China are more and more significant.Using the recent available input-output tables of China and energy consumption data,this study estimated the effects of Chinese foreign trade on carbon emissions and the changes of the effects by analyzing the emissions embodied in trade between 2002 and 2007.The re-sults showed a more and more significant exporting behavior of embodied carbon emissions in Chinese international trade.From 2002 to 2007,the proportion of net exported emissions and domestic exported emissions in domestic emissions increased from 18.32% to 29.79% and from 23.97% to 34.76%,respectively.In addition,about 22.10% and 32.29% of the total imported emissions were generated in processing trade in 2002 and 2007,respectively,which were imported and later exported emissions.Although,most of the sectors showed a growth trend in imported and exported emissions,sectors of electrical machinery and communication electronic equipment,chemical industry,and textile were still the biggest emission exporters,the net exported emissions of which were also the largest.For China and other developing countries,technology improvement may be the most favorable and acceptable ways to re-duce carbon emissions at present stage.In the future negotiations on emissions reduction,it would be more fair and reasonable to include the carbon emissions embodied in international trade when accounting the total emissions of an economy.     

中国城市碳排放强度的空间溢出效应及驱动因素探究（英文）

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained.(1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing.(2) The spatial autocorrelation Moran's I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable.(3) Spatial Markov chain analysis shows a Matthew effect in China's urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear "Spatial Spillover" effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa.(4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.     

中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素分析（英文）

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.     

中国石化产业空间组织的评价与优化(英文)

The spatial organization of the Chinese petrochemical industry was optimized ac-cording to the status of development of the industry employing linear programming and Ar-cGIS spatial analysis tools. We first identified the indexes of the spatial organization of the petrochemical industry and established a comprehensive evaluation index system that in-cludes four major categories and 11 indicators. The weight of each index was then deter-mined by the analytical hierarchy process. Afterward, taking the 337 Chinese prefecture-level administrations as basic units and scientifically evaluating the potential comprehensive layout coefficients of the cities, 151 prefecture-level administrative units were selected as the basis for the choice of optimization sites with a linear programming model. Secondly, using the 151 prefecture-level administrative units and the maximum-coverage model, the optimal number and spatial distribution of refineries were identified for service radii of 100, 200 and 300 km. Thirdly, considering the actual distribution of China’s refineries, general rules for the number of refinery layout points and objective values were summarized, and 52 refinery layout points were selected for China. Finally, with ArcGIS spatial analysis tools, the spatial effect of the 52 optimal refinery layout points was simulated for the service scope and socioeconomic factors respectively, and the GDP and population data for each refinery layout point were then ex-tracted within the service scope. On this basis and with estimation of the intensity of crude-oil consumption, final results were obtained for the optimal spatial organization of the Chinese refining capacity and ethylene production capacity.     

中国在气候变化下碳排放、能源消费与低碳经济领域的研究进展综述（英文）

Accompanying the rapid growth of China's population and economy, energy consumption and carbon emission increased significantly from 1978 to 2012. China is now the largest energy consumer and CO2 emitter of the world, leading to much interest in researches on the nexus between energy consumption, carbon emissions and low-carbon economy. This article presents the domestic Chinese studies on this hotpot issue, and we obtain the following findings. First, most research fields involve geography, ecology and resource economics, and research contents contained some analysis of current situation, factors decomposition, predictive analysis and the introduction of methods and models. Second, there exists an inverted "U-shaped" curve connection between carbon emission, energy consumption and economic development. Energy consumption in China will be in a low-speed growth after 2035 and it is expected to peak between 6.19–12.13 billion TCE in 2050. China's carbon emissions are expected to peak in 2035, or during 2020 to 2045, and the optimal range of carbon emissions is between 2.4–3.3 PgC/year(1 PgC=1 billion tons C) in 2050. Third, future research should be focused on global carbon trading, regional carbon flows, reforming the current energy structure, reducing energy consumption and innovating the low-carbon economic theory, as well as establishing a comprehensive theoretical system of energy consumption, carbon emissions and low-carbon economy.     

空间溢出视角下经济集聚对水污染物排放的影响——基于中国 339 个地级及以上城市数据的实证分析（英文）

Whether economic agglomeration can promote improvement in environmental quality is of great importance not only to China's pollution prevention and control plans but also to its future sustainable development. Based on the COD(Chemical Oxygen Demand) and NH3-N(Ammonia Nitrogen) emissions Database of 339 Cities at the city level in China, this study explores the impact of economic agglomeration on water pollutant emissions, including the differences in magnitude of the impact in relation to city size using an econometric model. The study also examines the spillover effect of economic agglomeration, by conducting univariate and bivariate spatial autocorrelation analysis. The results show that economic agglomeration can effectively reduce water pollutant emissions, and a 1% increase in economic agglomeration could lead to a decrease in COD emissions by 0.117% and NH_3-N emissions by 0.102%. Compared with large and megacities, economic agglomeration has a more prominent effect on the emission reduction of water pollution in small-and medium-sized cities. From the perspective of spatial spillover, the interaction between economic agglomeration and water pollutant emissions shows four basic patterns: high agglomeration–high emissions, high agglomeration–low emissions, low agglomeration–high emissions, and low agglomeration–low emissions. The results suggest that the high agglomeration–high emissions regions are mainly distributed in the Beijing–Tianjin–Hebei region, Shandong Peninsula, and the Harbin-Changchun urban agglomeration; thus, local governments should consider the spatial spillover effect of economic agglomeration in formulating appropriate water pollutant mitigation policies.     

1980-2017年中国碳强度关键影响因子变化特征

The Chinese government ratified the Paris Climate Agreement in 2016.Accordingly,China aims to reduce carbon dioxide emissions per unit of gross domestic product(carbon intensity)to 60%–65%of 2005 levels by 2030.However,since numerous factors influence carbon intensity in China,it is critical to assess their relative importance to determine the most important factors.As traditional methods are inadequate for identifying key factors from a range of factors acting in concert,machine learning was applied in this study.Specifically,random forest algorithm,which is based on decision tree theory,was employed because it is insensitive to multicollinearity,is robust to missing and unbalanced data,and provides reasonable predictive results.We identified the key factors affecting carbon intensity in China using random forest algorithm and analyzed the evolution in the key factors from 1980 to 2017.The dominant factors affecting carbon intensity in China from 1980 to 1991 included the scale and proportion of energy-intensive industry,the proportion of fossil fuel-based energy,and technological progress.The Chinese economy developed rapidly between 1992 and 2007;during this time,the effects of the proportion of service industry,price of fossil fuel,and traditional residential consumption on carbon intensity increased.Subsequently,the Chinese economy entered a period of structural adjustment after the 2008 global financial crisis;during this period,reductions in emissions and the availability of new energy types began to have effects on carbon intensity,and the importance of residential consumption increased.The results suggest that optimizing the energy and industrial structures,promoting technological advancement,increasing green consumption,and reducing emissions are keys to decreasing carbon intensity within China in the future.These approaches will help achieve the goal of reducing carbon intensity to 60%–65%of the 2005 level by 2030.     

1990–2010年中国耕地变化对粮食生产潜力的影响分析(英文)

The quantity and spatial pattern of farmland has changed in China, which has led to a major change in the production potential under the influence of the national project of ecological environmental protection and rapid economic growth during 1990–2010. In this study, the production potential in China was calculated based on meteorological, terrain elevation, soil and land-use data from 1990, 2000 and 2010 using the Global Agro-ecological Zones model. Then, changes in the production potential in response to farmland changes from 1990 to 2010 were subsequently analyzed. The main conclusions were the following. First, the total production potential was 1.055 billion tons in China in 2010. Moreover, the average production potential was 7614 kg/ha and showed tremendous heterogeneity in spatial pattern. Total production in eastern China was high, whereas that in northwestern China was low. The regions with high per unit production potential were mainly distributed over southern China and the middle and lower reaches of the Yangtze River. Second, the obvious spatiotemporal heterogeneity in farmland changes from 1990 to 2010 had a significant influence on the production potential in China. The total production potential decreased in southern China and increased in northern China. Furthermore, the center of growth of the production potential moved gradually from northeastern China to northwestern China. The net decrease in the production potential was 2.97 million tons, which occupied 0.29% of the national total actual production in 2010. Third, obvious differences in the production potential in response to farmland changes from 1990 to 2000 and from 2000 to 2010 were detected. The net increase in the production potential during the first decade was 10.11 million tons and mainly distributed in the Northeast China Plain and the arid and semi-arid regions of northern China. The net decrease in the production potential during the next decade was 13.08 million tons and primarily distributed in the middle and lower reaches of the Yangtze River region and the Huang-Huai-Hai Plain. In general, the reason for the increase in the production potential during the past two decades might be due to the reclamation of grasslands, woodlands and unused land, and the reason for the decrease in the production potential might be urbanization that occupied the farmland and Green for Grain Project, which returned farmland to forests and grasslands.     

20世纪80年代以来中国土地利用变化的基本特征与空间格局(英文)

Land-use/land-cover changes(LUCCs) have links to both human and nature interactions. China's Land-Use/cover Datasets(CLUDs) were updated regularly at 5-year intervals from the late 1980s to 2010, with standard procedures based on Landsat TM\ETM+ images. A land-use dynamic regionalization method was proposed to analyze major land-use conversions. The spatiotemporal characteristics, differences, and causes of land-use changes at a national scale were then examined. The main findings are summarized as follows. Land-use changes(LUCs) across China indicated a significant variation in spatial and temporal characteristics in the last 20 years(1990–2010). The area of cropland change decreased in the south and increased in the north, but the total area remained almost unchanged. The reclaimed cropland was shifted from the northeast to the northwest. The built-up lands expanded rapidly, were mainly distributed in the east, and gradually spread out to central and western China. Woodland decreased first, and then increased, but desert area was the opposite. Grassland continued decreasing. Different spatial patterns of LUC in China were found between the late 20th century and the early 21st century. The original 13 LUC zones were replaced by 15 units with changes of boundaries in some zones. The main spatial characteristics of these changes included(1) an accelerated expansion of built-up land in theHuang-Huai-Hai region, the southeastern coastal areas, the midstream area of the Yangtze River, and the Sichuan Basin;(2) shifted land reclamation in the north from northeast China and eastern Inner Mongolia to the oasis agricultural areas in northwest China;(3) continuous transformation from rain-fed farmlands in northeast China to paddy fields; and(4) effectiveness of the "Grain for Green" project in the southern agricultural–pastoral ecotones of Inner Mongolia, the Loess Plateau, and southwestern mountainous areas. In the last two decades, although climate change in the north affected the change in cropland, policy regulation and economic driving forces were still the primary causes of LUC across China. During the first decade of the 21st century, the anthropogenic factors that drove variations in land-use patterns have shifted the emphasis from one-way land development to both development and conservation. The "dynamic regionalization method" was used to analyze changes in the spatial patterns of zoning boundaries, the internal characteristics of zones, and the growth and decrease of units. The results revealed "the pattern of the change process," namely the process of LUC and regional differences in characteristics at different stages. The growth and decrease of zones during this dynamic LUC zoning, variations in unit boundaries, and the characteristics of change intensities between the former and latter decades were examined. The patterns of alternative transformation between the "pattern" and "process" of land use and the causes for changes in different types and different regions of land use were explored.     

基于能源消费的中国不同产业空间的碳足迹分析

Using energy consumption and land use data of each region of China in 2007,this paper established carbon emission and carbon footprint model based on energy consumption,and estimated the carbon emission amount of fossil energy and rural biomass energy of dif-ferent regions of China in 2007.Through matching the energy consumption items with indus-trial spaces,this paper divided industrial spaces into five types:agricultural space,living & industrial-commercial space,transportation industrial space,fishery and water conservancy space,and other industrial space.Then the author analyzed the carbon emission intensity and carbon footprint of each industrial space.Finally,advices of decreasing industrial carbon footprint and optimizing industrial space pattern were put forward.The main conclusions are as following:(1) Total amount of carbon emission from energy consumption of China in 2007 was about 1.65 GtC,in which the proportion of carbon emission from fossil energy was 89%.(2) Carbon emission intensity of industrial space of China in 2007 was 1.98 t/hm2,in which,carbon emission intensity of living & industrial-commercial space and of transportation in-dustrial space was 55.16 t/hm2 and 49.65 t/hm2 respectively,they were high-carbon-emission industrial spaces among others.(3) Carbon footprint caused by industrial activities of China in 2007 was 522.34 106 hm2,which brought about ecological deficit of 28.69 106 hm2,which means that the productive lands were not sufficient to compensate for carbon footprint of industrial activities,and the compensating rate was 94.5%.As to the regional carbon footprint,several regions have ecological profit while others have not.In general,the present ecologi-cal deficit caused by industrial activities was small in 2007.(4) Per unit area carbon footprint of industrial space in China was about 0.63 hm2/hm2 in 2007,in which that of living & indus-trial-commercial space was the highest (17.5 hm2/hm2).The per unit area carbon footprint of different industrial spaces all presented a declining trend from east to west of China.     

Three-dimensional modelling of soil organic carbon density and carbon sequestration potential estimation in a dryland farming region of China

Soil organic carbon density(SOCD) and soil organic carbon sequestration potential(SOCP) play an important role in carbon cycle and mitigation of greenhouse gas emissions. However, the majority of studies focused on a two-dimensional scale, especially lacking of field measured data. We employed the interpolation method with gradient plane nodal function(GPNF) and Shepard(SPD) across a range of parameters to simulate SOCD with a 40 cm soil layer depth in a dryland farming region(DFR) of China. The SOCP was estimated using a carbon saturation model. Results demonstrated the GPNF method was proved to be more effective in simulating the spatial distribution of SOCD at the vertical magnification multiple and search point values of 3.0×10~6 and 25, respectively. The soil organic carbon storage(SOCS) of 40 cm and 20 cm soil layers were estimated as 22.28×10~(11) kg and 13.12×10~(11) kg simulated by GPNF method in DFR. The SOCP was estimated as 0.95×10~(11) kg considered as a carbon sink at the 20–40 cm soil layer. Furthermore, the SOCP was estimated as –2.49×10~(11) kg considered as a carbon source at the 0–20 cm soil layer. This research has important values for the scientific use of soil resources and the mitigation of greenhouse gas emissions.     

中国农业劳动力变化与农业经济发展及其对乡村振兴的启示（英文）

Based on panel data from 1991, 2000 and 2010 at the county level in China, this study analyzed the coupling characteristics and spatio-temporal patterns of agricultural labor changes and economic development under rapid urbanization using quantitative and GIS spatial analysis methods. Three primary conclusions were obtained.(1) During 1991–2010, China's agricultural labor at the county level showed a decreasing trend, down 4.91% from 1991 to 2000 and 15.50% from 2000 to 2010. In spatial distribution, agricultural labor force has evolved by decreasing eastward and increasing westward.(2) During 1991–2010, China's agricultural economy at the county level showed a sustained growth trend, with a total increase of 140.13%, but with clear regional differences. The proportion of agricultural output in national GDP gradually decreased, characterized by decreases in eastern China and increases in western China.(3) The coupling types of economic-labor elasticity coefficient are mainly growth in northwest China, for both the agricultural economy and labor, and are intensive in southeast China, with growth of the agricultural economy and reduction of agricultural labor. Regions with lagged, fading, and declining coupling types are generally coincident with the high incidence of poverty in China. However, different coupling types had a positive developing trend for 1991–2010. Finally, based on the coupling types and spatial distribution characteristics of economic-labor elasticity coefficients, some policy suggestions are proposed to promote the integration of the primary, secondary, and tertiary industries and the vitalization of rural economies.     

中国地区植被对温度变化趋势的响应（英文）

Though many studies have focused on the causes of shifts in trend of temperature, whether the response of vegetation growth to temperature has changed is still not very clear. In this study, we analyzed the spatial features of the trend changes of temperature during the growing season and the response of vegetation growth in China based on observed climatic data and the normalized difference vegetation index(NDVI) from 1984 to 2011. An obvious warming to cooling shift during growing season from the period 1984–1997 to the period 1998–2011 was identified in the northern and northeastern regions of China, whereas a totally converse shift was observed in the southern and western regions, suggesting large spatial heterogeneity of changes of the trend of growing season temperature throughout China. China as a whole, a significant positive relationship between vegetation growth and temperature during 1984 to 1997 has been greatly weakened during 1998–2011. This change of response of vegetation growth to temperature has also been confirmed by Granger causality test. On regional scales, obvious shifts in relationship between vegetation growth and temperature were identified in temperate desert region and rainforest region. Furthermore, by comprehensively analyzing of the relationship between NDVI and climate variables, an overall reduction of impacts of climate factors on vegetation growth was identified over China during recent years, indicating enhanced influences from human associated activities.